Advisory Boards

Comment Policy, Social Guidelines

We try to avoid risk and often miscast uncertainty for risk. While they're both a fact of life, it's valuable to understand the difference between the two. Uncertainty and risk are related concepts in economics and the stock market.

The concepts are related, but not the same. You cannot avoid risk, every act of creation involves it. Even not doing anything has a risk component. But you can learn to move away from uncertainty, especially when you're called to make important decisions.

Dynamic nature of risk

Since risk is defined as unknowns that have measurable probabilities, we can insure against it. But not all risks are insurable. Because uncertainty involves unknowns with no measurable probability of outcome.

Risk has odds, while uncertainty doesn't. Causation is the dimension of risk we need to factor in our thinking, along with the behavioral pattern or risk frequency and severity. How often and how bad is it?

In risk management we have a simple framework we use to evaluate risk along three dimensions of risk classification:

1. Financial and non-financial — can we measure it in monetary terms?

2. Pure and speculative — is the outcome always a loss, or could there be a gain?

3. Fundamental and particular — what is the relationship of cause and effect? Who causes it?

Within this classification, however, things change all the time. Because risk is not static, it's dynamic by nature. Frequency and magnitude factor in the calculations. Where you are and the environment around you make a difference.

Data on risk frequency and severity helps insurance companies figure out predictability of future events. It also helps the individual figure out the cost to insure. Severity or the answer to, “could this be catastrophic?” drives whether to insure or not.

What happens when we confuse risk and uncertainty

Risk, as first articulated by the economist Frank H. Knight in 1921, is something that you can put a price on. Say that you'll win a poker hand unless your opponent draws to an inside straight: the changes of that happening are exactly 1 in 11. This is risk. It is not pleasant when you take a 'bad beat' in poker, but at least you know the odds of it and can account for it ahead of time. In the long run, you'll make a profit from your opponents making desperate draws with insufficient odds.

Uncertainty, on the other hand, is risk that is hard to measure. You might have some vague awareness of the demons lurking out there. You might even be acutely concerned about them. But you have no real idea how many of them there are or when they might strike. Your back-of-the-envelope estimate might be off by a factor of 100 or by a factor of 1,000; there is no good way to know. This is uncertainty.

Risk greases the wheels of a free-market economy; uncertainty grinds them to a halt.

The context Silver used for his comparison is the alchemy the rating agencies performed leading up to the financial crisis of 2008. They spun uncertainty into what looked and felt like risk. We lived the consequences of those decisions.

When risks become hazards

In thinking about risk as dynamic, we look at probabilities. Frequency and severity are typically inversely proportional. Some things happen frequently but are not severe, some are severe but happen seldom.

However, there is a type of risk that happens frequently with enough severity to warrant noticing. There are conditions or situations that are likely to increases the chances of a loss from a peril we've identified. We call this a hazard.

Physical and moral hazards influence the likelihood or a poor outcome from risk. Hazards are like fuel — they can accelerate the gravity of a situation. Since we can identify physical hazards, we can insure against their variables.

But what happens when we're talking about moral hazard? Character, integrity and mental attitude are not easy to measure. Behaviors can be unintentional or not consciously bad and still remain hazardous.

There are rules and regulations that govern insurance to risk. But we've seen what happens when we overreach and accumulate too much risk. A collapse of confidence followed the last financial crisis that was catastrophic for many.

Why complexity requires new thinking

To improve the quality of our decisions, we need also to understand the dynamics that surround risk and uncertainty.

Our attempts to predict the future are too reliant on linear thinking based on past experience#. In a complex world, past data may not be as useful to forecast risk, things change fast. We're also limited in our ability to handle probabilities.

Retired economist and Singaporean government official Lam Chuan Leong# says we can classify the way we think and manage events in a two-by-two matrix by combining the findings from Behavioral Economics and Complex Systems.

Time and complexity are the vectors. When we're called to make decisions but have little time, we behave differently than when we have more time. In our daily activities, we decide using our intuitive system, automatically and quickly.

Give us more time, and we can reflect on decisions. We use reasoning and analysis. In economics terms, this is the Rational Man Model of thinking. But this may not be enough. Cause and effect are not as consistent in complexity.

Lam Chuan Leong outlines the four modes of thinking that apply to many of our decisions in business and are influenced by time and complexity:

But it's not just how we should think about risk that has changed. Our work has also changed. All the easy problems are taken, and problem-solving now involves few certainties and right answer.

“We have to make sense of the problem by probing, experimenting, creating environments conducive to the generation of new ideas and new interactions, and responding to emerging patterns and behaviors,” he says.

This in turn has changed how we manage in a complex world.

Challenges require critical thinking

As we're increasingly called to probe, sense and respond, we need to create an environment and experiments where innovative patterns can emerge. The increased levels of interaction and communication should be designed to generate ideas and options.

Leaders are setting boundaries, barrier and incentives to encourage patterns to grow into coherence and general acceptance instead of merely executing the plan.

But with managing risk there are also cognitive and behavioral challenges:

Key biases include our desire to rationalize into a story when we want to explain a decision, even though emotion is involved in decision-making, and anchoring — for example, start with a high number, and you're now willing to pay a higher price.

We also have confirmation bias, we see what we want to see, and ignore evidence that contradicts our beliefs. People react to a particular choice in different ways depending on how it is presented — e.g., as a loss or a gain.

While in the past we could rely on analysis and control of risk, we need new ideas and innovation to solve future problems. When we think about issues, we should take into account the environment where we operate and its complexity.

Beyond recognizing our cognitive biases and acknowledging our time constraints, we need to take into account how emotions connect to outcomes in decision-making.

We manage to ignore all the noise, including much potentially useful information and data on the facts, yet we pay attention to stories and snippets that trigger our dramatic instincts. That's because we get an emotional high from them.

Our filter has ten holes in it. Hans Rosling named them in Factfulness. Fear is the most used among them — the others are gap, negativity, straight line, size, generalization, destiny, single perspective, blame, and urgency. Fear is hard-wired into our brain, we fear missing out (FOMO) and we have a strong fear of failure, making a mistake.

We should learn to distinguish something that is “frightening” from something that is “dangerous”. One is perceived, the other is real risk.

Why trends are useful

Trends help us create a framework around variables we track over the long term. In Megatrends, John Nasbitt says that each new technology, to be successful, must be coupled with a compensatory human response. Thirty years later, we can tell the trends because they endured in our culture.

The three main categories of fear: are fear of physical harm, fear of captivity — as in loss of control or loss of freedom — and fear of contamination. They distort our worldview and lead to horror of things we don't fully understand or deal with in every day life, like infection and poison.

When stories combine two fears, like that of a plane crash and kidnapping — tapping into fear of harm and captivity — we're pulled in. Then we get programmed by hearing it over and over again seemingly everywhere. This is a distraction that keeps us from learning the deeper data behind the story and taking action, if appropriate.

Yet the predictive story points to a larger trend that is cause for optimism. That is the story we want to let in our attention filter.

Mimicry and unknown unknowns

The majority of the stories we get in the media are the local and familiar kind. They merge facts with anecdotes to share things that are of potential human interest, that connect with belief. They trigger us by appealing to our dramatic instincts.

Sensational headlines draw attention, but distort reality and keep us setting apart what we know from what we don't know. Multiply by the avalanche of similar articles chasing attention, which include many containing false data, and pretty soon it becomes hard to tell a mimic from the real thing.

Batesian Mimicry#is a form of mimicry where a harmless species has evolved to imitate the warning signals of a harmful species directed at a predator of them both. For example, we should avoid the venom of the Texas Coral Snake while the Mexican Milk Snake and other types of snakes that look similar# are not as bad.

Beyond the things we already know, there are things we don't know we should know about. But once we're aware of them, we can learn those things because they're knowable. We can learn to tell the snakes apart, for example. Known known and known unknown are two instances of certainty.

There are also things we know we don't know, we've identified we don't know them and others we don't know we don't know. The diagram from the Project Management Institute# calls out the nature of risk — uncertainty creates the need for understanding risk.

You can't manage what you don't understand

Recently, I had a meeting with a group of executives who were looking to evaluate a number of approaches to deal with a situation that involved risk. They had created a brief that called both for preparation and optimization.

After some conversation I realized that they already had some kind of expectation of what the approach would need to look like. Yet, by their own admission, they did not have a full understanding that the brief and the expectation where not on the same page. There was also domain knowledge uncertainty.

Their attention was focused on short term tactics, yet the request was for a strategic long term approach. When the focus becomes not the topic but the expectation, the results is a disconnect. The hole (or more than one) in our attention filter leads us to confuse perception with reality.

In Rosling's definition, risk equals actual danger times our degree of exposure. It's very hard to evaluate something when we have a hole in our knowledge and don't know we do. Said another way, risk is the possibility of suffering loss or harm, not the loss itself.

When we learn to manage the sources of exposure to risk, including our own reactions (for example, fear) and experience with uncertain situations, we can begin to benefit from the opportunity brought about by change and creative problem solving.

To manage risk appropriately, we should remember that if the nature of an occurrence is certain, it is more like a fact or knowledge, If it's uncertain (probability of is less than 1, for example), the impact can be uncertain as well.

Evidence and data are useful in decision-making, they also keep us from stressing out and feeling helpless.

Greater choice paralyzes us and makes us poor strategists. This is something we all struggle with in our work and personal lives. There are way too many options to choose from with too little time in a day. When we're done picking a technology marketing stack, multitasking, and ducking in and our of meetings, there's no energy left to pick a toothpaste.

Do we really need hundreds of products with very little differences? Is it useful personally to become an expert in wine, cereal, and boxed pizza? When we're faced with a wall of choices, how do we pick? Is it worth to have a strategy?

Users and buyers do have strategies, even when they're not aware of them. Organizations and brand strategies should go beyond the obvious — competing on the shelf and in the market — and get to the heart of what matters to customers by seeing what isn't there.

How we choose

Offers and price breaks typically work to sway us on commodity products. In other cases, we tend to trust a store brand — for example Trader Joe's label and Whole Foods 365 both have organic product families. Fewer options make them easier to select. Let them filter the choices.

Most of us have but a few minutes to get what we need at the store at the margin of our days. Which means a few seconds per product. Maybe we look at expiration dates, maybe we scan the list of ingredients, but few wade into comparison territory. If we're researching and/or buying online product reviews do have an impact.

Yet brands are obsessed with comparison — sometimes using the competitive set to get to a place of parity with others, rather than differentiation. Yet when we let brands filter for us, we do it on the basis of a very specific story they tell us, and we tell ourselves.

Youngme Moon says most brands that focus too much on market segmentation and product augmentation, while attempting to create differentiation, have actually led to meaning less distinctions. Competitive analysis and comparative metrics have created conformity, resulting in competitive herding, with all competitors blurring together in the mind of the customer.

Moon offers some high level ideas on creating effective differentiation. For example, subtract features from the offering, creating difference by stripping away the superfluous. Or polarizing, so that some people hate the offering while others love it. Clever marketers have also transformed an offering into a different category in the perception of customers.

How we differentiate

It's true that a clearer value proposition, a choice by the brand manager, is compelling. True differentiation, however, starts with a clear understanding of the market gap the product supports and the business model that supports the product.

This works for service as well, by the way, where it's a combination of things that make it us, us and not another [insert your professional title.] This past week I was talking with a hiring manager who told me “basically what I'm looking for is a unicorn.”

But could not say which 3 of the two dozen things on her list matters the most. Yet, it's those 3 that matter the most to the success of that unicorn in getting work done best. The multiple talents and plays at once unicorn is just that, a rare (and expensive) thing. Betting on a horse who can win a certain kind of race creates more advantage.

Because that's how we buy products, too. In the end we pick the one that meets our top 2-3 requirements to get the job done and call it a good day. Google differentiates on the basis of a product and business model. Find what you need as quickly as possible. IKEA makes design affordable, you put in the labor... or pay a little more to have it assembled.

How we tell our story

Once we we learn to see what isn't there, and create a product or service that adds value, we want to find the best way to communicate that value. This means figuring out the specific language we use to talk about how we help. Something like:

[Organization/Product/Service] is a [adjective]+[short description (should include a term that describes the most important thing)] built for [who we serve].

Maybe our value is for experts, maybe it's to support everyday choices. How we do what we do can be different. For example, we ship a kit to your house every month#. Maybe we buy at a store that sells everyday items online unbranded#.

With [Organization/Product/Service], [who we serve] can [job it does] + [core differentiator].

Or maybe we link the jobs to be done to customer segments. This works for businesses that sell to other businesses (B2B). Increasingly, users (the ultimate customers) drive the purchasing decisions of intermediaries as well.

For example, installers care about ease of use of features and how they fit existing or complementary systems. Contractors may have favorites. Ultimate users, the customers who get an addition may care about price and availability, maybe deterioration rate. People with building product experience may know to look for innovation in air flow as well.

Before we can sell a feature, we need to connect the user with the benefit at a deeper level. It starts by seeing what isn't there. People don't necessarily know or care to learn more... unless we lift the veil of ignorance of why it matters to them specifically.

Culture is a also a factor. The people on the inside of an organization — product or marketing manager, engineering, etc. — may care about certain things. The people outside it — users, customers, social group, etc. — may care more about others. We must know the difference and plan accordingly.

We often make decisions based on “good enough” information. Our brain is configured to make us act fast and without definite proof. Intuition uses what we call rules of thumb to select an item. The best experiences use this gut instinct to help us do the right thing easily.

For example, the roundabout in many cities in Europe is designed to keep traffic moving, instead of stopping it. It works based on a simple rule of thumb — if anything comes from the right, you stop. Another example of a rule of thumb we use — if something feels too good to be true, it probably is.

Nobel Memorial Prize in Economic Sciences Daniel Kahneman is notable for his work on the psychology of judgment and decision-making. He wrote about this phenomenon in Thinking Fast and Slow. “Good enough” is our System 1 at work, it can help us generate complex patterns of ideas quickly but may be too fast to draw good conclusions. Our slower System 2 gives us the ability to think things through.

An automatic response to information and situations often serves us just fine in many situations. Human behavior is not mathematical. Yet, in our strategy discussions we focus mostly on the things that we can represent numerically. Instinct is grossly undervalued.

Signaling

Science has demonstrated that people act based on things like signaling. Companies and brands spend money to build reputation to signal they will be around for a long time. It might not matter as much for products we consider based on convenience and price — we have our online convenience stores, open 24/7, and the algorithm knows what to recommend.

Individuals signal something with their purchases — we're environmentally conscious by buying an electric car or installing solar panels. Or we want to signal status with luxury purchases. Trying to appeal to reason with data is futile, we use data only to rationalize we made the right choice after the purchase.

Often the brand story stops with the transaction, a missed opportunity to reinforce the story. Brands are proxies that compress data into a story. Inversely, what is the message when organizations don't invest in signaling?

Experiences start with a story we tell, the why behind a product or service — vegan fast food, essential clothing without markup, designer eye-wear without breaking the bank, a modern day version of a roadside burger stand, ready to wear luxury, and so on.

We do make impulse purchases, and our constant connection with a limitless inventory of options online means that availability at the point of need — right place, right time — is also a signal. This includes the impact to perception based on the company brands keep#.

Focusing decision-making

Not all decisions involve purchasing a product or service, but an idea. In many of these decisions, “fine” (or good enough) can include a healthy dose of luck, which leads to overconfidence. We're all prone to an exaggerated sense of how well we understand the world, especially experts, says Kahneman.

Sometimes, good enough requires a slower thought. Tip to the wise: frowning is a simple way to engage our slower thinking System 2.

When we become more aware of the interplay of the fast System 1 and the slower System 2 in our thinking, we can improve our odds of making better decisions. We also don’t make our decisions in a vacuum. As our context changes, our decisions may need tweaking, if not rethinking.

Context is not just external, we also drive it. For example, what works for us in our twenties may not work so well in our mid-thirties. That’s also because we update what we think are our taste and ideas based on intervening experiences, as we learn (or so we hope.)

Three things must happen at once for us to do something — 1./ we want to do it; 2./ we're able to do it; 3./ we're prompted to do it. The story provides the priming, how fast or slow we act depends on the type of prompt.

B.J. Fogg#, founder and director of the Stanford Behavior Design Lab, says, “when motivation is high enough, or a task easy enough, people become responsive to triggers such as the vibration of a phone, Facebook’s red dot, the email from the fashion store featuring a time-limited offer.”

Willpower can go only so far, and we can answer the design of a system that could lead us down a rabbit hole in search of more, with a system of our own design optimized for better. This means using System 2 to help us focus decision-making.

Thinking in horizons

Our thinking also has horizons. We live in the here and now, enjoying our experiences, and also think about the future, even when it feels distant and abstract. Both ways of thinking are important to realizing what we want in life — our goals and our dreams.

Longer-term thinking tends to help us build a more flexible structure to absorb what life throws at us. Resilience is the ability to survive the shocks. Learning continuously and a longer horizon are ways to edge our bets.

“All civilizations suffer shocks; only the ones that absorb the shocks survive,” says humanist Stewart Brand[1]. In recent years a few scientists have been probing the same issue in ecological systems to understand how they manage change, absorbing and incorporating shocks.

“The answer appears to lie in the relationship between components in a system that have different change-rates and different scales of size. Instead of breaking under stress like something brittle, these systems yield as if they were soft. Some parts respond quickly to the shock, allowing slower parts to ignore the shock and maintain their steady duties of system continuity.”

Imagine a series of layers, like a cake, each somewhat independent and also interacting with the one closest to it. Stewart Brand says all durable dynamic systems have such a structure with interactions happening between faster layers and slower (more core) layers.

Mathematician and physicist Freeman Dyson makes a similar observation about human society. He talks about six time scales, with a twist — “the unit of survival is different at each of the six time scales.” According to Dyson, the individual is on a time scale of years, while culture is on a time scale of millennia.

To survive, we need to juggle the demand of different time scales, sometimes in conflict with each other.

“Every human being is the product of adaptation to the demands of all six time scales. That is why conflicting loyalties are deep in our nature. In order to survive, we have needed to be loyal to ourselves, to our families, to our tribes, to our cultures, to our species, to our planet.”

Acting fast and slow

Steward Brand proposes six significant levels of pace and size in the working structure of a robust and adaptable civilization, from fast to slow they are:

Fashion/Art

Commerce

Infrastructure

Governance

Culture

Nature

Like every dynamic system, it works based on feedback.

As we grow wiser, we tend to gravitate toward the slower layers, but we can continue to experiment and learn through the fast ones. Fashion is culture running around and trying new things. Hence fads, which come and go.

Commerce is fashion’s enabler. It helps sift trends from fads. Trends stay with us for a while, becoming part of the fabric of society. The farther from the surface, the longer the payback. Education and science are infrastructure in this model. Culture moves at the pace of language and religion.

Fast and slow are built into all life forms and human activities.

Things that are fast lead to things that are slow. To give examples close to my heart, high enjoyment comes from slow food, smart travel starts with fast reach, la dolce vita sits on the surface of work mastery. Italian style is art with brains.

Finding the answer of what works — the combination of fast and slow that works — happens with the help of time. A community of like minds and a network of connections do create a robust infrastructure to stay the course and absorb the shocks. Hence why we gravitate toward people who act like we think.

In many cases it's difficult to know what people actually think, even for the people themselves. We look at signaling through story and experience.

Thinking fast and slow, coupled with a “dynamic performance mindset,” as Dr. Constance Goodwin would say, creates the premise for the stuff that happens between thought and action — comprehension, synthesis, articulation, and conversation.

“Specialization is for insects. Humans need the mystifying ability to cope with the unpredictable and ambiguous challenges posed by thinking adversaries in the real world.”

[Robert Heinlein]

It's an interesting question that may not have as binary an answer as we would like. We need specialists, and we need strategists.

Can someone be a little bit of both?

Since the early '90s recruitment included the concept of T-shaped skills — a metaphor used to describe the abilities of individuals. Depth of expertise and related skills in a single field represented by the vertical bar on the T, and the horizontal bar indicates ability to collaborate across disciplines with experts in other areas and to apply knowledge in areas of expertise other than one's own.

In the early days of social media and with the flourishing of digital experiences and culture, the metaphor was used as a way to describe an ideal combination of depth and breadth. It's particularly useful in consulting and technical fields, which rely more heavily in dynamic feedback loops.

A a strong understanding of the system as a whole can be very valuable to organizations.

Who is a specialist?

A specialist is an expert or devoted to a particular brand of research — if you're having an eye surgery, you want to go to a physician who specializes in that particular organ, or even issue, because they'll have more experience under their belt.

How we should think about specializing.

“Everyone should learn programming” is a statement that has been making the rounds. Knowing how to code is valuable, but the advice is incomplete if we don't know how to apply the value to something. On the other hand, if you learn to develop web sites using specific WordPress themes like Genesis or Divi, for example. That is something specific.

Learning how to code is a good way to acquire strong analytical thinking skills, learning logic workflows and debugging through an activity that is very intertwined with modern business. We can apply those skills to many situations beyond actually building apps or sites.

But keep in mind real world applications when thinking about becoming a specialist.

Because as software developer Jeff Atwood says,“[Coding] puts the method before the problem. Before you go rushing out to learn to code, figure out what your problem actually is. Do you even have a problem? Can you explain it to others in a way they can understand? Have you researched the problem, and its possible solutions, deeply? Does coding solve that problem? Are you sure?”

This happens in many professions and fields — the world needs more people who know how to define better solutions than the ones we have now. Coding is how you solve the problem. But first you need to define what the problem is.

Experts are taking a beating in current mainstream conversations, but there is tremendous value in true depth of experience and practice in a field. This is especially true as industries and professions mature. Specialization is not static. There's a constant need to stay up to speed and update skills based on new information.

Who is a strategist?

To understand better what strategists do, it's useful to understand what strategy is.

Col. John Boyd defines it as, “A mental tapestry of changing intentions for harmonizing and focusing our efforts as a basis for realizing some aim or purpose in an unfolding and often unforeseen world of many bewildering events and many contending interests.” More definitions of strategy here.

Strategists deal with complexity and time as vectors. Their skill is the ability to use new information to update decisions on a course of action to reach an aim.

Complexity is a measure of relationships. One thing depends on another. Complex systems are a network of things that are connected and do interesting things like anticipate the future and adapt.

Some examples:

dispersed control - like the Queen bee in a beehive

interacting agents - as in the behavior of many individuals who bring with them their own motivations, their own mental models, their own problem-solving strategies, e.g. the Stock Market

non-linearity - built on small elements that tend to interact and have much larger consequences, like the butterfly effect

adaptation - or the ability to anticipate the future by lots of agents, like a flock of birds

exhibit perpetual novelty - like the immune system that is constantly trying to predict what your body is being attacked by

The weather is a complex, adaptive system. The best we can do is make a prediction of what might take place based on what we currently know about the system. To keep in front of issues, we update our predictions based on new information.

To do that, we use the premise contained in Bayes' theorem that uses math to describe the probability of an event based on what we already know to update the probability based on new evidence or information.

Strategists are systems thinkers who scan the market to uncover new patterns and data points, challenge assumptions and test hypotheses as they balance past and future to frame issues. Their skills include synthesis, storytelling, and the ability to engage multiple perspectives.

+

Whether we're specialists or strategists, a better question is how our choices are creating a fulfilling career that also helps make the world a better place. Understanding the differences in focus is useful to help us acquire the right skills and experience.

Our brains are amazing organs — they allow us to infer information, get us to safety, solve problems, and imagine things. Some of these feats help save our lives, others merely assist in saving our face. We do dumb things, too, but we have something that only humans have — symbolic reasoning.

While there's a lot we still don't know about the three-pound gray matter that sits as the body's command center in its armor, the hard skull, we do know a few things that can help us make better use of it. But before reading further, let's all take a walk around the office, house, or block.

Movement makes us think better. Moving around, 10-12 miles a day between walking and running, is how our ancestors survived and evolved. In Brain Rules John Medina says, “Though a great deal of our evolutionary history remains shrouded in controversy, the one fact that every paleoanthropologist on the planet accepts can be summarized in two words: we moved. A lot.”

Among the things we do know about how the brain works, 12 stand the test of time:

Brain Rule #1 — survival, the human brain evolved, too

The brain appears to be designed to solve problems related to surviving in an unstable outdoor environment, and to do so in near constant motion.

In fact, we adapted to change itself. After we were forced from the trees to the savannah by climate swings to find food. Switching from four to two legs, our energy freed up to develop the complex brain. Crawling and climbing helped our convergence for when we acquired a different view.

Our three brains are the “lizard brain” to keep us breathing, a brain like a cat's, and the thin layer we call “cortex” on top to power human intelligence. Symbolic reasoning is a talent only humans have. Maybe trying to understand each other's motivations and intentions helped along. The benefit was our ability to get together and act as groups. (With mixed reviews to this day.)

Brain Rule #2 — exercise boosts brain power

If our operating system was to develop, walking and moving round 10-12 miles per day helped it along. Want to improve thinking skills? Move more. Exercise gets blood to our brain, which delivers glucose for energy and oxygen to mop up the toxic electrons left over from the process. It also stimulates the protein that keeps neutrons connecting.

Two times a week of aerobic exercise cuts in half the risk of general dementia, and the risk for Alzheimer by 60 percent. Getting a dog is a good way to ensure long walks twice a day. And we get a loyal companion.

Brain Rule #3 — sleep well, think well

When we're asleep, our brain is quite active, maybe replaying our learning of the day. What happens in our brain is fascinating — cells and chemicals in constant tension between going to sleep and staying awake. Saying, “get some sleep” seems simple.

But some of us require more sleep than others. We do all have one thing in common — the biological need for an afternoon nap. Research has found that a 20-minute nap between 2-3pm is optimal.

Brain Rule #4 — stressed brains don't learn the same way

Stress is our built-in defense system to avoid danger. Our brain's release of adrenaline and cortisol are life saving. Nature designed this kind of stress to be temporary. When it becomes chronic, adrenaline scars our blood vessels, which can cause heart attack, stroke, or do more damage to our ability to learn and remember.

It's the one thing we don't want to keep long term, because it's designed to be a short-term response. A feeling of helplessness is the worst kind of stress. Emotional stress has the worst impact on our ability to function well.

Brain Rule #5 — every brain has different wiring

We're a combination of genetics and environment. Each of us develops regions of the brain differently based on what we do and learn in life. At two and in our teens, neurons experience a growth spurt and pruning, but no two people store the same information the same way... and in the same place.

IQ is not the only kind of intelligence, many more are invisible in its tests. So not so good a measure.

Brain Rule #6 — we don't pay attention to boring things

Whether our brains are different because or gender or not, which is still an open question, we're all better at seeing patterns and abstracting the meaning of an event. This is our faster System 1 at work to help us make sense of things on the spot.

All those job descriptions that talk about ability to multitask demonstrate a failure to understand how our attention works — we can focus only on thing at a time. Even computers start rendering in the background when we try to open too many apps at once.

Brain Rule #7 — memory, repeat to remember

Memorizing things works. Because our brain collects memory in busy work spaces that function like temporary way stations, repeating things helps us store them more permanently. The best way to make long-term memory more reliable is to introduce new information gradually, then repeat at timed intervals. Long-term memory is a conversation between the hippocampus and the cortex.

Our process for making memories is similar to how we program artificial intelligence using machine learning. We have many types of memory systems. Explicit memory follows four stages — encoding, storing, retrieving, and forgetting. Incoming information gets broken down and dispatched to different parts of the cortex for processing.

Each of us has only an approximate view of reality — we mix new knowledge with past memories to form our worldview.

Brain Rule #8 — sensory integration, stimulate more of the senses

We take in information from different paths — sight, sound, touch, taste, etc. create electrical signals that go to different parts of the brain to reassemble and give us a sense of what happened. Past experiences seem to influence how we reconstruct reality. This is why two people witnessing the same event will have different stories.

Our senses work together, which means that learning something through more than one method is best. Smell is the strongest recorder of past memories — it has a direct line to the amygdala, which supervises emotion.

Brain Rule #9 — vision beats all other senses

It takes up to 50 percent of our brain's resources and is not even 100 percent accurate. That's because vision has many steps. Our retina assembles protons into streams of information that look like a movie. The visual cortex takes them in to process as motion, color, etc. Then we piece everything together in what we call sight.

Pictures are the best way to remember.

Brain Rule #10 — music boosts cognition

Formal musical training improves intellectual skills in several cognitive domains, including the social sphere. This means a finer ability to detect emotion in speech, improving cognitive empathy and other social behaviors at any age.

Music boosts spatial-temporal skills, vocabulary, picking up sounds in noisy environments, working memory, and sensory-motor skills.

Brain Rule #11 — gender, male an female brains are different

The X chromosome is a cognitive “hot spot” that carries a large percentage of genes that manufacture the brain. Females have two, males have one (and one acting as backup.) If you've wondered, are men's and women's brains different? Yes, they are in structure, but we don't know if this has significance.

Women's genetics are more complex as the active X chromosomes are a mix of Mom's and Dad's. Men's X chromosomes all come from their mother and their Y chromosome carries less than 100 genes (the X has 1,500.) Our response to stress is different — women remember the emotional details, men get the gist.

Brain Rule #12 —exploration is powerful and natural to us

Watch a baby interact with the environment and you see how we learn. We observe, formulate a hypothesis, experiment, and draw conclusions. It's one and the same with the scientific process. Specific parts of the brain are in charge of each phase.

The right prefrontal cortex looks of errors in our hypothesis, as in “that snake is going to bite us,” a region next to it tells us to “Run!” for our lives. Mirror neurons help us become better negotiators, as they allow us to recognize and imitate behavior.

Of all the things we know about the brain, these 12 principles will help us improve how we do things to make the best use of our operating system.

Three ways to use this knowledge

1. Move around more throughout the day — park at the end of the lot, take stairs where possible, have walking meetings, join a hiking, walking, running, or any outdoors activity you can on weekends or early in the morning.

If you live in Europe, congratulations, you may able to take more public transportation, bike, train, or even walk to work. If you live in a city that is well connected through reliable public transportation, you can do the same.

2. Experiment with your circadian rhythms — often referred to as the “body clock,” the circadian rhythm is a cycle that tells our bodies when to sleep, rise, eat—regulating many physiological processes. We “open” and “close” at regular times during the day (circa means around, diem means day.) They're built-in, and they adjust to the local environment via external cues. But we do have predictable patterns.

In When, Dan Pink reports on research that helps us figure out the optimal times for us to focus our attention to get which things done, when to sleep, and how to nap. During the day, we can pin down the best times to concentrate and use our sharpest analytic capacities, ideate, or let our imagination loose on a question or problem to get to an insight, and rest. Ready?

To understand where to optimize your efforts, you need to figure out what time you go to sleep, what time you wake up, and the middle point. For example, you go to bed at 10:30pm, wake up at 6:30am, your midpoint is 2:30am. Then go find yourself on the Roenneberg chart below.

Then adjust your tasks accordingly. Morning people (larks), do analytic tasks early in the morning, insight tasks in late afternoon/evening, and make decisions early in the am. Early birds can go to mid-morning for analytic and otherwise follow the same patterns as larks. Owls should defer analytic tasks to and make decisions in late afternoon and early evening, insight tasks in the morning.

Everyone should prepare to make a good impression in the morning. Getting at least 7 hours of sleep is important. Naps are very valuable. Taking a 20-minute nap between 2-3pm has been proven to recharge us.

3. Sequence work, rather than multitasking — in my linguistic analysis of job descriptions, the use of the word “multitasking” is both a cultural/social signal and lazy. It's a mechanical way organizations use to regard work as industrial effort, which creates busy bodies and activity. But we know activity does not equal results. One good clue as to the culture is to calculate the number and format of meetings and analyze results or outcomes.

It's lazy because no organization wants to create the perception people slack there (do your deep thinking in your spare time, already!) Broader culture dictates that hustling, grinding, and gritting are good and does not distinguish when it's appropriate to do so — timing and purpose are everything, in work, and in life.

We can all benefit from having more data on hand about our habits. Diets are a simple example, and still relevant in January of a New Year. Regardless of which program works for you, part of it is creating more efficiency between what we consume and what we accumulate in the system.

The word is appropriate, because we're talking about a system of interconnected parts. Although it's tempting to jump on the fad bandwagon and clean house every so often, the best approach to create lasting change and improve wellness is the judicious way, following a personalized nutrition program. What works for one person, doesn't work or apply across the board to the other.

There are some universal principles we can adopt. So many calories in and more calories out if we want to lose weight. Counting calories by writing everything down is a way to become aware of what we consume, taking our weight weekly, a way to see outcome and adjust based on the information.

But the principle itself has no information about what and how our specific body works. We may have allergies, experience intolerance for certain foods that although great for others, actually slow down our metabolism.

Nutrition is a serious discipline that requires some study and research, but also experience i practice. And it's not just about weight control, but helping make sense of the chemistry, which comes down to knowing how to read the data, learning about our personal context (lifestyle), and cross referencing to data from a larger group of people.

A new book by Lisa Mosconi helps us understand the relationship between our cognitive power and what we eat. In Brain Food (out in March), Mosconi, who unsurprisingly grew up in Italy, reviews brain science, the microbiome, and nutritional genomics, noting that the dietary needs of the brain are substantially different from those of the other organs.

The benefits of a better diet are clear, and the answer is not fillers in packaged goods. It's always my test when I go to a supermarket — how many ingredients are listed on the package? Made in Italy is easy to tell, few and recognizable. In fact, whole grain and organic foods are available at low cost at Coop in Italy and even Switzerland (though everything is more expensive there).

Nutrition value labels, list of ingredients, and place of origin are all data points we likely use when we go grocery shopping to make sense of what's available. How many of you go to more than one store or supermarket, as available?

Four markets and two specialty stores are in my rotation, one fairly distant — but quality eating at a reasonable price is worth every penny in gas and time. Trader Joe's does have a surprisingly high number of products imported from Italy. We do love our simple food, and knowing what's in it.

Imagine if you had visibility into what foods stores plan to stock across stores, instead of having to physically go (many still catching up online), and check. Many families build their weekly shopping routines based on proximity. What would happen if more data and information about inventory and even best times of day/week to shop were available for a certain geography?

Most food chains have loyalty programs, yet until very recently there did not seem to be an intelligent use of purchasing habits — coupons were pretty random, and I'm not talking about Target and the famous case of targeting expecting women in The Power of Habit.

From what markets stock on the shelves, we know what sells. But we don't know what would sell, were it available. It would be fascinating to learn about potential trends from foods people buy in aggregate, and forecast local taste and opportunity, for example. We can infer some of the data from restaurants — but in the U.S. things have become quite standardized, which is both good and bad for research purposes.

Data has value to a community

The point, beyond the incontrovertible fact that I am a foodie — hence the aggressive exercise regiment — is that data is valuable to understand what makes a community, well, a community.

Data geeks and industry professionals likely do collect all kinds of data on their household, maybe comparing with their family in other states of countries — we do — but most of us would benefit even more with a broader set, with evidence for their county, say.

However, data sets sit with different companies in different systems, none of which we, the subjects, have access to it. So we could not even apply a better analysis and technology to it than what each specific company has chosen to use, which may or may not provide insights, depending on who is doing the analysis and why.

It's a bit of a pickle, pun intended, because it could really help us make better decisions if we had more visibility into what's going on in our region. A good example of this dilemma emerged from Amazon's HQ2 bids. As the New York Times chronicles#, the tech giant benefited from a treasure trove of data on each community that submitted an entry.

Each city put its best foot forward, outlining technical schools, number of faculty, access to specific demographics, and even adding discretionary incentives, normally not on the books, “special grant funds, special bond funds, naming of roads,” things nobody can find or learn, unless they're put together in a bid of this kind.

It was a great way to start amassing data on physical assets otherwise not available to cross reference with digital data trails. But what about the communities themselves? This is also an opportunity for the community to do a deeper dive about Denver#, an entry made public.

In fact, the difference between the Denver bid and a promotional site (see Philadelphia# for an example), can help us see the value of data, and not just benefits and sound bites, to educating a community and illuminating possibilities, beyond an Amazon's HQ office.

We've become accustomed to data going one way, from us to organizations, and hardly think about the value of aggregate data to our own community. While anecdotal evidence we experience of what sells, how much construction is going on, traffic volumes, peak gym season, new schools, and so on, is useful, we tend to notice what is available — hence availability bias — and miss what we don't know.

Better/more data is useful for so many decisions we make in life. Even the position of a house in a block can make a big difference to snow drift or foliage collecting on the lawn — small things that over the years do increase friction and maintenance.

Same for where the windows are relative to sunlight, and how big a switch is your power on (hint: it it's small, pick a house on a switch with more houses, you'll have the power back sooner, or not even lose it.) Imagine now having the big picture on traffic trends for the past ten years — where they're increasing, which roads take the brunt, etc. etc.

On the pragmatic side, we can start by becoming more aware of the positive role of data in our lives (and not just organizations' content cherry picked to appeal to us) and seek to be more observant, withholding premature judgment by cross referencing it with other data.

If you'll pardon one more abstraction to the big picture, learning to think better starts with ingesting better data (vs. opinion), and cross referencing it across disciplines. Working from actual data and first principles is better. Sound bites can sound neat, but end up biting us in surprising ways.

This is why it's a good idea to get as close to the source as possible — actually read Socrates, and Homer, and the classics from many cultures, the foundational works from many disciplines. We want to learn ourselves how to form an opinion, which involves principles, values, and proof.

It's helpful to have a guide, but think of it more like cooking than baking. Don't stop at the recipe — better to use as a guideline to get curious about a dish, improve it by adding of personal taste and reference points. Simpler works for my palate, but this doesn't exclude layers of flavor.

Now imagine that community is an organization, and the data highlights behaviors. Then imagine what it would be like to cross reference that data on behaviors with data about customer experience based on outcomes. How valuable would it be to learn what is helpful and what detracts in aggregate and cross reference it with outcomes?

Success is a product of the interplay between luck and skill. But we often confuse the two, or are uncertain about which one is which. For better results we need to be able to distinguish one from the other, ahead of time.

“There's a quick and easy way to test whether an activity involves skill,” says Michael Mauboussin, “ask whether you can lose on purpose.”

Skill is the ability to fire knowledge readily in performance and execution. We know how to do something, and when the moment comes, we can do it.

Luck has three specific features — it works for an individual and/or organization, it can be good or bad, and it's reasonable to expect something else could have happened.

In The Success Equation, Mauboussin says that when skill is predominant in a field, the best course of action is to engage in deliberate practice with feedback and coaching; while when luck is predominant, he advises not to worry over results, because we have little or no control over them. Instead we should just focus on getting our process right to succeed long term.

Even when we understand the definitions of luck and skill, we have a hard time attributing things that happen primarily because of luck to it. We're engineered to think it was us, that skill was responsible.

Brain's fallacy

Our nature throws a wrench in our ability to distinguish luck from skill. The problem is that we naturally embrace stories and shy away from statistics. He says:

“While most of us are comfortable acknowledging that luck plays a role in what we do, we have difficulty assessing its role after the fact. Once something has occurred and we can put together a story to explain it, it starts to seem like the outcome was predestined.

Statistics don't appeal to our need to understand cause and effect, which is why they are so frequently ignored or misinterpreted.

Stories, on the other hand, are a rich means to communicate precisely because they emphasize cause and effect.”

This is partly due to our brain. In the left brain hemisphere we have an area called “interpreter” that constantly looks to make sense of what happened in the background. It assimilates everything we perceive and interprets that input to form a cause-and-effect narrative within our sphere of self-image and beliefs.

It doesn't know about luck, so it gravitates towards likely explanations and misleads our logical side. It doesn't help that we have a natural ability to act with confidence based on scant information.

Hindsight bias, which tells us that what happened is the only thing that could have happened keeps us locked into our story. So we lose track of other potential explanations or possibilities. Which is why we're not very good at sorting skill from luck.

Another reason makes the distinction hard.

Skill-luck continuum

Skill and luck are not fast categories, but are on a continuum, says Mauboussin.

For example, chess takes skill to play well and win, and gambling takes luck on the other end of the spectrum. For the rest of things that happen in life, we are caught between the two extremes. How we make decisions and predictions both depend on where we are on that continuum.

When we analyze sports, we find that some have a higher luck component, like hockey or the NFL due to shorter seasons, and others rely on higher skills, like basketball. Baseball is somewhere in the middle, with 162 games in a season.

Interestingly enough, investing is far on the luck side. It's counter intuitive because people are very skilled. But everyone else is also very skilled. So the variance or difference between results narrows considerably.

The idea Mauboussin introduces is that in a new market or sport, participants have big differences in ability. Which creates opportunity for the most skilled participants who sometimes have extraordinary success. For example, the .400 hitters in the earlier days of baseball.

However, in a mature market like investing, more skilled people have joined the ranks, and the existing participants have improved their abilities. Which elevates the overall skill but makes the difference in skill almost disappear. So the remaining variation is mostly due to luck.

We can have more success with less skill if we're an early entrant in a market. So our best chance of success is to do something few others are doing, changing the game, rather than trying to beat the established players at their own game. This is where we talk about unfair advantage.

Where we are on this continuum of skill-luck has enormous implications for decision making and predictions.

Key concepts

A few concepts in The Success Equation are worth highlighting for a greater understanding of how difficult it can be to tell luck from skill:

The paradox of skill— In fields where skill is more important to the outcome, luck's role in determining the ultimate outcome increases. While in fields where luck plays a larger role in the outcome, skill is also very important but difficult to ascertain without a large enough sample set.

IQ vs. RQ — Intelligence Quotient (IQ) is an overused talent measure for success because we associate the Rationality Quotient (RQ) more closely with decision making. Many people hide behind “good work product,” an IQ outcome, rather than evaluating “good decision making,” which is more about RQ. RQ involves adaptive behavioral acts, judicious decision making, efficient behavioral regulation, goal prioritization, the ability to be reflective, and proper calibration of evidence.

The Matthew Effect — is the idea that the most successful individuals tend to grow more successful while the poor grow poorer due to the endowment effect of early success, which may be due to good luck and not just skill.

Favorites should simplify the game — if we have superior resources, we should try to concentrate our battles in fewer fields than if we are the underdog. Various studies show that increasing complexity increases the role of luck and gives the underdog an advantage in competition while diluting the advantage of the stronger player.

Fluid vs. crystallized mind — research shows that our fluid mind, the part of our brain useful for creative decisions when facing problem sets we have not seen, decreases with age at an accelerating rate. The crystallized mind, our ability to develop mental models for “problems we have seen before,” tends to increase well into old age. This is also true of organizations. Statistical analysis offers clear evidence that corporate performance follows a predictable life cycle, falling prey to organizational rigidity with age.

Clutch performance — streaks have some empirical support but this topic hasn't been as well studied from a behavioral perspective as it has been from an observational standpoint.

We can outperform based on skill, it's statistically supported by data. But we still need luck to turn high skill into success. In fields where luck still plays a dominant role, we must understand what it means to be the best:

high IQ

elevated RQ with a strong system 1 for pattern recognition and a non-lazy system 2 for logical processing (based on Daniel Kahneman and Amos Tversky's research; see Thinking Fast and Slow)

fluid mind that allows us to think creatively about opportunities using unique, differentiated framing

well-tuned crystallized mind that correctly identify patterns and uses well-developed mental models to make decisions with the information

clutch instincts to make difficult decisions even under pressure when presented with new, adverse, or fortuitous information

Business Applications

Recognizing where an activity lies on the luck-skill continuum can shape strategy, help us hone skills and deal with uncertainty, and improve performance in numerous ways that are relevant to business. We should:

consider the sample size — if an activity is falls mainly under the control of luck, a small sample will not do

understand history — helps more in skill-based activities than in luck-based activities

categorize events — if they have simple/complex payoff and narrow/extreme outcome

It's also important to recognize whether the process is dynamic or not and to understand how reversion to the mean works.

When an activity is a mix of luck and skill, less extreme performances tend to follow extreme ones, good or bad. They tend to revert to the mean. If we praise someone for good performance, which then declines or if we tell someone about bad performance, which then improves, we might conclude it was because our intervention was counterproductive in one instance and beneficial in the other. But that would be wrong.

Mauboussin also points out how incentives reward factors that are generally luck-based and have little to do with an organization's real success. For example, stock options can reward poor performers when overall market prices rise and punish good performers when overall market prices drop.

Feedback is an essential part of improving performance. In a dynamic system, feedback helps us continue to adjust our aim to keep getting things less wrong. In dealing with our automatic system 1 in decision making we can work on:

analyzing how to do things properly

appreciating the psychology of the effort

understanding the influence of the social system where we work

If we want to improve performance we must be aware of our automatic pilot and create habits that help us get out of our own way in decision making. Discipline is our ally.

The idea is not to have equal parts luck and skill, but to balance luck with skill and vice versa based on the situation and what it warrants.

Much of our modern dialogue about business and many other topics today is based on the dichotomy between science and advocacy. We say evidence, consistency, and proof are important to us, but we also have strongly held beliefs the truth of which ironically prove hard to explore objectively.

That's because we're not objective at all. We're built that way, and we hardly ever gather data on ourselves... plus how much is the data gathering process unbiased? We tend to think we're proficient at something even when we aren't. And the “we aren't” is likely closer to the truth.

Our ego fights fiercely to defend its honor — this is one idea Freudian therapists and experimental psychologists agree in. “As a result, many of our most basic assumptions about ourselves, and society, are false,” says Leonard Mlodinow in Subliminal.

Mlodinow asks a series of provocative questions like, Why doesn't the business executive question his/her own abilities when the group or division is not meeting the numbers? Why don't the professionals who never seem to shed the modifier in front of their title wonder how they're not progressing? How do we convince ourselves we're the best drivers on the road? That we have talent when we don't?

Psychologist Jonathan Haidt observed that there are two ways to get at truth:

Attorneys begin with a conclusion they want to convince others of and then seek evidence that supports it, while also attempting to discredit evidence that doesn't.

The human mind is designed to be both a scientist and an attorney, both a conscious seeker of objective truth and an unconscious, impassioned advocate for what we want to believe.

Together, these approaches vie to create our worldview.

We don't know what we don't know, which makes it hard as we try to frame questions well. But also when we go around trying to prove we're right, that's a poor recipe for decision-making. That's a problem and it doesn't serve us most of the time, even when we factor luck.

That's because, as it turns out, according to Mlodinow:

the brain is a decent scientist but an absolutely outstanding lawyer. The result is that in the struggle to fashion a coherent, convincing view of ourselves and the rest of the world, it is the impassioned advocate that usually wins over the truth seeker.

The unconscious mind is a master at reconstructing a view of the world with scant data. It's a good thing when we're called to infer information quickly, say to save our lives, or to deduct a pattern early on and save resources that are precious energy-wise.

Deductive reasoning — along with imagination — is the reasons why Big Data should not replace thinking, especially of the strategic kind. If it's true that, everything around us that we call life, was made up by people that were no smarter than us, as Steve Jobs once said in an interview, it's also true that our senses and processing power construct this reality from “a mix of raw, incomplete, and sometimes conflicting data.”

Self-awareness is very hard for this reason. Our unconscious mind uses the same outstanding lawyer-skillet to create our image of self borrowing liberally from a collage of facts and also illusions. We should also be mindful that facts share the same root with manufactured, just for good measure.

So we literally construct our self and our reality based on an interpretation. When empathy levels are low or absent, compassion is not present, we succumb to more narcissistic tendencies. But it gets worse, because the rational agent in our mind, the scientist who lives in the conscious mind, then admires this self-portrait somehow believing it to be true. This is how our ignorance sabotages us.

Curiosity can help us become more scientists and serve up better information to our conscious mind. Neil deGrasse Tyson says, “Science literacy is more, how is your brain wired for thought? Are you wired for curiosity?” Because science is all around us, all we need to do is get away from our own “motivated reasoning,” as psychologists call it, which helps us feel in control by casting a very positive image of who we are, but deludes us by doing so.

And it prevents us from getting better, growth mindset or not. Rationally, we know the impossibility of 40 percent of engineers squeezing into the top 5 percent as we'd like to believe, or 60 percent of students to fit in the ten percentile, or for 94 percent of college professors to fit into the top half. But we often do convince ourselves with ambiguity as our ally.

Ambiguity is our tool of the convincing trade, it creates uncertainty of meaning. It gives us wiggle room, allowing us to interpret things on more than one way. The ambi part of the word means “two meanings,” but it can be more than two. Open to interpretation doesn't mean vague, however, so there is that.

We do interpret experiences differently, even when the experience itself is the same — a football game, a concert, a sermon or a presentation, and so on. Even in science, research and evidence are subject to interpretation, more easily in social sciences... and likely highly correlated with interests. Which is why advancement in so many fields happens when the people who have a stronghold on a theory or course of action die.

In other words, when we want to see something, we work really hard to see it. Motivation is why when we deal with complex issues, like what constitutes ethical behavior, or running a business, or our ability to get along with others, our unconscious mind takes its pick to feed to the conscious self, says Mlodinow.

Study upon study demonstrates how so many professionals who think they're unbiased are in fact very much so. The unconscious mind is quite sneaky. It puts one over us by automatically indulge our wants, needs, and desires. That's when it's firing on all cylinders.

motivated reasoning involves a network of brain regions that are not associated with “cold” reasoning, including the orbifrontal cortex and the anterior cingulate cortex — parts of the limbic system — and the posterior cingulate cortex and precuneous, which are also activated when one makes emotionally laden moral judgements.

We physically bypass reason! But our conscious minds are no fools, so the distortion cannot be extreme, it needs some semblance of objectivity to slip past us. And here's where the mind jumps in to help maintain the illusion.

For example, many studies on hiring based on resumes and interviews have now demonstrated how biased organizations are based on gender, choosing to justify the skill sets they need retroactively to fit their worldview.

When the world tries to enter the gateway of our mind, if the information is favorable, we put up an easy fight to let it in. If the data contradicts us, we ask it to spell an impossible word to get in, as it would happen in an old joke.

This is the same mental mechanism people use to find serious issues with trivial hearsay if it helps them condemn a situation or person they disagree with, and embrace their choice despite strong evidence that it's at fault — person or belief. For example, global climate change, which has been proven beyond reasonable doubt as detailed in Merchants of Doubt, and yet still so many find reason to doubts its veracity.

Scientists actually agree on that point now, it's an issue that is very much settled. But it's not good news, so people create wiggle room by believing a different story. What can we learn from all this? Are there ways to use this knowledge to improve our ability to think and act or decide by seeking evidence that also disproves our thesis?

To keep ourselves honest, and calmer in the face of disagreements with others, or biased hiring decisions that may backfire, we should be mindful that:

people who disagree with us may not be necessarily duplicitous, or dishonest, in their refusal to acknowledge errors in their thinking, which is obvious to us

our own reasoning may be biased to lean in the direction of what we want and need to be true

walking in someone else's shoes is useful to see the world through their prism — when called to analyze data before taking sides in a dispute, people tend to increase their willingness to agree at the tune of from 28 to 6 percent in legal disputes

having higher standards for the kind of supporting evidence we need to reach conclusions or make decisions, rather than adjusting them to our wants is useful to uncover new opportunities — including seeking evidence that contradicts our thesis

creating a realistic timeline on projects based on the reasonable time it takes to complete each step or task in the chain, rather than our desire to finish a job earlier helps us deliver quality work — experience can help us here, but we should account for decisions that are outside our control as well

believing in ourselves is good, it pushes us to accomplish greater feats with optimism — how we get there is also important

The first principle is that you must not fool yourself—and you are the easiest person to fool. So you have to be very careful about that. After you’ve not fooled yourself, it’s easy not to fool other scientists. You just have to be honest in a conventional way after that.

I would like to add something that’s not essential to the science, but something I kind of believe, which is that you should not fool the layman when you’re talking as a scientist. I’m not trying to tell you what to do about cheating on your wife, or fooling your girlfriend, or something like that, when you’re not trying to be a scientist, but just trying to be an ordinary human being. We’ll leave those problems up to you and your rabbi.

I’m talking about a specific, extra type of integrity that is not lying, but bending over backwards to show how you’re maybe wrong, that you ought to do when acting as a scientist. And this is our responsibility as scientists, certainly to other scientists, and I think to laymen.

He then provides an example that touches on science and business:

One example of the principle is this: If you’ve made up your mind to test a theory, or you want to explain some idea, you should always decide to publish it whichever way it comes out. If we only publish results of a certain kind, we can make the argument look good.

We must publish both kinds of result. For example—let’s take advertising again—suppose some particular cigarette has some particular property, like low nicotine. It’s published widely by the company that this means it is good for you—they don’t say, for instance, that the tars are a different proportion, or that something else is the matter with the cigarette. In other words, publication probability depends upon the answer. That should not be done.

Taking this approach also helps us as scientists in the broadest sense of conscious beings, not be used by others for their ends. This is important. If we learn not to fool ourselves, which is the easiest thing to do, we also learn not to be fooled by others.

“We choose our facts based on what we believe. We also choose our friends, lovers, and spouses not just because of the way we perceive them,” says Mlodinow, “but also because of the way they perceive us.” It's a sure bet that we'll choose survival over anything else.

We may not realize it, but to make decisions in our personal and professional lives we often use negotiation techniques. In many of our important decisions — those that have the greatest impact on our performance at work and your satisfaction at home — most of us need to reach out to others and negotiate.

Experienced negotiators learn to focus on issues rather than harden on positions, and in so doing they engage in joint problem solving. In Getting to Yes, Roger Fisher and William Ury of the Harvard Negotiation Process say the definition of wise agreement is “one that meets the legitimate interests of each side to the extent possible, resolves conflicting interests fairly, is durable, and takes community interests into account.”

When we're operating under normal circumstances, these kinds of negotiations work well. But we're increasingly immersed in a context where volatility, uncertainty, complexity, and ambiguity have made their home. Stress and strain increase our emotional temperature and may fuel inflexibility in unexpected circumstances due to our desire for some form of control over our destiny.

To achieve cooperation at work and at home, we need to overcome real-world barriers. William Ury wrote about the five most common ones in a follow up book, Getting Past No. Many if not most of them involve appreciating the perspective of the other party/ies.

Empathy can help us meet them where they are in the way we react, and respect their emotions, position, dissatisfaction, and mental model for power.

— Reactions. We're designed to be reaction machines. Under stress, when facing a NO, or feeling we're being attacked, our natural reaction is to strike back. But this type of reaction creates a vicious cycle with two losers. Another impulsive reaction is to just give in, avoid confrontation and preserve the relationship. This is common in families, but also among colleagues. When we give in we “lose,” and may open the door to exploitation by others who see us as weak. To address the behavior by the other side, we first and foremost need to address the part our own behavior, how we react, plays in the situation.

“You need to suspend your reaction when you feel like striking back, to listen when you feel like talking back, to ask questions when you feel like telling your opponent the answers, to bridge your differences when you feel like pushing for your way, and to educate when you feel like escalating.”

— Emotion. The next barrier is the other party's negative emotions. Behind the attacks are probably anger and hostility, and behind their rigid positions fear and distrust. It's easy to convince ourselves of anything under the influence of strong emotions. They may be convinced they're right and we are wrong, refuse to listen, and feel justified in using underhanded tactics. Strong emotions generate aggression and are hard to address head on lest we intend to bump into a wall. We also don't want to fall into the trap of raising our own emotional temperature.

“Speak when you are angry and you will make the best speech you will ever regret,” says Ambrose Bierce

— Positions. In joint problem-solving, two or more parties face the problem and attack it together. The barrier to overcome is the other party's digging into a position and trying to move us to give in. By holding firm, the thinking goes, they'll get what they want. It's something we learn in the school of life growing up. This is an unsophisticated method but it persists because they perceive the choice as binary, they either win or give in.

— Dissatisfaction. Our goal may be to reach a mutually satisfactory agreement, but we may find the other side not at all interested in this type of outcome. Maybe they don't see how it will benefit them. They may fear losing face if they have to back down, even when they get what they wanted. Understanding the other person's perspective in important, including how they may not embrace and idea because it comes from us.

Here we want to remain aware of the other party's needs for recognition, or security, how they tie their identity to the situation and ensure that the proposal is consistent with their principles and values.

— Power. In a dog-eat-dog world, the mindset creates a win-lose proposition as the only option available. This means winning by beating us. The precept may be more, “What's mine is mine. What's yours is mine,” than “negotiable.”

Here we can help the other party see how reaching an agreement is in their interest and see alternatives by asking questions like, “What do you think will happen if we don’t agree?” “What do you think I will do?” “What will you do?”

Silence is an important tool to use in conversation where power is a main concern. It also helps diffuse tension when we remain calm, ask open-ended questions, and let the other person speak in their own time.

Getting past no is a good primer to organize our thinking and not fall prey of a closed mindset that ascribes fixed traits to the other party. We want to give them the benefit of being heard, rather than approaching the conversation with the idea we can do little or nothing to change their behavior.

To affect behavior we need to deal successfully with the underlying motivations. Ury provides five techniques we can use to get past “no”:

1. Don't react — Go to the balconyWhen we view any negotiation from a third person's point of view we remove emotion and have a better vantage point from which to see the picture. “Stay focused on your end goal and keep an eye on the prize,” reminds us of something Steve Jobs said in 1997 as he began to turn Apple around.

2. Disarm them — Step to their sideWe should use counter-intuitive moves to diffuse the tension and avoid bumping into walls. Sometimes this means agreeing with our figurative opponents to get things done. By stepping to their side, we build on “yes,” and create a more positive feeling.

4. Make it easy to say yes — Build a golden bridgeIn Asian cultures this is the equivalent of helping the other party save face. Hence the Chinese sage, “Build a golden bridge.” When we try to understand how our offer benefits the other person we have an easier time working toward it. The idea is to “Help write your opponent's victory speech,” and everything else will fall into place.

5. Make it hard to say noInstead of bringing the other party to their knees by pressuring them to say “yes,” it makes more sense to make it harder to say “no” by using our power to educate. In this case, you come out of the negotiation without one lesser friend/prospect, with the possibility of a future agreement. “The best general is one that never has to fight.”

We've all been in situations when the other party refuses to budge, or to talk. When we learn to view an irascible boss, a temperamental teenager, a hostile co-worker, or an impossible customer as a potential partner... or at least with empathy and compassion, we can avoid unpleasant escalations.